Radio-frequency (RF) environments in which signals or RF emissions of interest are to be distinguished from among other RF emissions to be detected, monitored, or analyzed, present increasingly difficult challenges for designers of signal-analysis systems. This is due in no small part to the growing utilization of the RF spectrum by the general public, whether intentionally, or unintentionally. As an example of a practical application, consider the task of threat detection, such as detection of weapons-targeting RADAR signals. Unimportant signals, such as civilian communications, civilian RADAR usage, and the like, tend to clutter the spectrum to be monitored for the presence of signals of importance (SOI). In dense RF environments signal-analysis systems may become overwhelmed, or saturated, and thus unable to keep up with processing each and every detected signal to distinguish the SOI from the non-SOI observable RF emissions.
Conventional approaches have relied on limiting the instantaneous bandwidth of signal analysis systems. As a consequence, such systems suffer in their visibility into the spectrum of interest as they commutate their instantaneous bandwidth. A practical solution is needed to facilitate the detection and analysis of signals of interest.